In many imaging applications (e.g., surveillance camera applications, thermal imaging applications, and/or other applications of cameras and imaging systems), a user may be more interested in observing terrestrial phenomenon and/or objects such as roads, people, cars, buildings, while less interested in the atmosphere or objects above ground (e.g., birds, planes, clouds, treetops, and/or other objects). However, for images (e.g., still images and/or video frames) captured by conventional imaging systems, the sky, if present, often consumes a large portion of the available dynamic range. This may make it harder for a user to discern or identify objects of interest in the captured images.
While there are conventional dynamic range compression algorithms or automatic gain control (AGC) methods that adjust image dynamic ranges, such conventional methods cannot reliably suppress the sky (e.g., reduce the consumption of the available dynamic range by the sky) in images. Those conventional methods fail, for example, because the sky captured in images is often non-uniform (e.g., may contain a large amount of scene information) and/or often exhibits different properties depending on atmospheric conditions, the climate, the sun angle, and/or other conditions.